Surveillance systems typically can preemptively warn security officers of potential threats and/or facilitate investigations of critical occurrences. For the latter capability, security personnel must proactively and forensically search video images for pertinent people or events. In practice, information regarding the appearance of a person or vehicle is often limited to characteristics that lack the permanence to identify the object uniquely and reliably. However, in the real world, such information can be very effective in pruning the search and helping to find the object, especially when used with other information.
When identifying people, such characteristics are often called “soft biometrics.” One of the most important soft biometrics is clothing color, since it is easy to observe and remember and feasible to extract from video images. Similarly, for identifying vehicles, color is often the only feasible cue. Thus, for many surveillance applications, color information can permit a user to find the significant event. For example, when following up on a report of a suspicious activity (e.g., locating a vehicle of a particular make, model and color that is involved in an incident) or when performing retail surveillance (e.g., associating a customer who took an item from a store with his or her exit) color information may assist the identification.
Identifying object color is a challenging problem because of the challenges of color constancy. For example, when looking at a full image of an object in context, the perceived (and true) color of the object may appear to be green. When a portion of the image of the object is isolated (e.g., extracting an unchanged portion of the full image), however, it may actually be composed of pixels of another color. For example, green pixels extracted from a full image may appear to as gray pixels or blue pixels in the isolated extracted portion. The pixels may “appear” green in the full scene because humans unconsciously observe that the scene has a red “cast” and compensate correctly to perceive green, the true reflectance of the object. Similarly, images from the same camera at a different time of day, or under different lighting conditions, may be perceived by a human to be another color and be composed of entirely different color pixels.
A need therefore exists for improved methods and apparatus for color correction of images.